One “Holy Grail” in the financial markets is the development of an automated system that predicts price movements of financial instruments. If one is able to predict whether prices were moving up or down for financial instruments such as stocks, bonds and commodities, then one would have a method of generating money. Several prediction strategies exist that find patterns in price fluctuations. They fall into two categories: fundamental analysis and technical analysis. Fundamental analysis is performed by an analyst that keeps abreast of the news and data affecting a specific stock or market. The successful analyst warehouses correlation in the market and predicts the correct trend. This type of analysis often involves a prediction with a long-term horizon, such as a few months or years. Technical analysis is performed by a person or machine that looks for numeric trends in the change in financial and economic measures. Technical analysis is often used for short-term and long-term trading. The following invention is a fusion of fundamental and technical analysis. The invention predicts the movement of a financial instrument given historical closing prices and daily financial news about the underlying financial instrument.
The Engineering and Economic literature is replete with approaches that use historical stock prices and economic values for predicting when to purchase a stock. For example, Yoon and Swales use a four-layered neural network to determine well performing firms and poorly performing firms using nine economic measures as input. [1] However, these approaches, whether they use neural networks or statistical regression, do not incorporate the events, and in particular, the news events that are responsible for the actual day-to-day price movements.
Economic news event studies have motivated several research projects. A typical event study would determine if a correlation exists between price changes and a particular event such as stock splits, merger announcements, or the reporting of earnings. The example on page A-5 in this document contains an example using takeover announcements. Other related research uses proxies for a more general classifications of news. For example, Depken [4] uses a decomposition of volume as a proxy for “Good” and “Bad” to study how split-stocks react to news. In this work and others, the measure of interest is the statistical variance of volume and price changes. However, it is not clear that event studies using variance or volatility as the measure of interest have predictive value. Volatility can be defined as the standard deviation (square of the variance) of the annual expected return of a security. By definition, volatility does not predict the direction of price movements, only a dispersion of possible annual returns, both negative and positive.
Upon close examination of the Economic event study literature, it is evident that prediction is not the purpose of the research. The motivation of this research is to find and explain a market behavior in the context of a correlation between specific events and price changes. Thus, much of the research does not provide results for prediction, or recommend how the techniques described could be used in a prediction process. See Chan [3] provides a comprehensive summary of other related research regarding Economic event studies.
There is some recent research from the Machine Learning and Information Retrieval literature that is concerned with prediction. This research attempts to correlate particular words in the news publications with subsequent price changes. For example, Fawcett and Provost [5] attempt to discover sets of words that often occur with 10% price changes in a stock. This type of text retrieval process shares a similarity to the invention described here, because it is extensible to events in general and not specific to predefined events. However, in this type of research, specific words predict when a particular price change event will occur, and there is no attempt to use an analyst's classification of “news” as input.